The broad, long-term objective of the proposed research is to develop statistical methods for medical image reconstruction that will enhance the accuracy of lesion detection and of quantitative measurement, two essential tasks in the effective diagnosis and treatment of cancer. The project will focus on development of new statistical methods for image reconstruction in positron emission tomography (PET), but the methods that will result may be applicable to other medical imaging modalities. Two themes will be emphasized: (1) development of new algorithms that employ statistical techniques to take a priori information into account in the reconstruction of medical images; and (2) use of spatially correlated images from multiple modalities as a source of a priori information. Bayesian methods incorporate a priori information into the reconstruction process for improving image quality. Substantial reduction in noise and improvement in spatial resolution can be achieved by the use of Gibbs priors to represent homogeneity within regions and boundaries separating them. Gibbs random field models and computational techniques will be developed to incorporate a variety of information in PET image reconstruction. A rich source of a priori information for reconstructing PET images is provided by spatially correlated high-resolution images from x-ray computer tomography (CT) and magnetic resonance imaging (MRI). Potential improvements in image quality are expected to allow extraction of new information that was unattainable previously from the reconstructed PET images. Success of the proposed research will result in improved methods for reconstructing PET images. These new methods will substantially reduce the noise, increase the spatial resolution, and improve the image contrast of the resulting images, thus enhancing the accuracy of lesion detection and region-of-interest localization. The accuracy and reproducibility of quantitative information derived from images reconstructed with the new algorithms will be improved as well. These improvements should assure more effective diagnosis and treatment of cancer. By providing improvements in image quality, the new PET reconstruction algorithms may also aid in the development of new diagnosis imaging or treatment techniques, such as receptor imaging for neurologic disorders, perfusion imaging for cardiac defects, and monoclonal antibody imaging for cancer detection and treatment. These new methods are also expected to provide improved quantitative information about fundamental biomedical processes such as metabolism and blood flow, especially those within small anatomic structures to which it has been difficult to gain access in the past. Thus, our knowledge about in vivo biochemical and physiological functions, both in health and in disease, will be enhanced.